An intelligent gradient-guided hybrid inpainting framework for brain MRI reconstruction and Alzheimer's disease classification in connected healthcare systems

Category	Detail
Condition	Alzheimer's Disease
Key Mechanisms	Hybrid inpainting framework combining classical diffusion-based methods and deep learning models to restore MRI images.
Target Population	Patients with Alzheimer's Disease across various stages of dementia.
Care Setting	Integrated healthcare networks utilizing brain MRI for diagnosis.

Proposed hybrid inpainting framework reduces mean squared error by 8% compared to LaMa and 30% compared to OpenCV.
Achieves SSIM ≈0.93 and PSNR ≈25.7 dB in image reconstruction.
VGG16 classifier trained on hybrid-inpainted data achieves 94.35% accuracy, showing minimal drop from baseline.
Method effectively preserves anatomical details and enhances classification performance.
Implementation demonstrates reproducibility with modest computational overhead.

Implement hybrid inpainting methods to improve image quality for automated classification.

Evaluate reconstruction quality and classification accuracy under various corruption settings.

Potential for structural inconsistencies in deep learning-based inpainting methods.

Individuals diagnosed with Alzheimer's Disease at varying severity levels.

Hybrid inpainting enhances diagnostic imaging quality, aiding in accurate disease classification.

Combine classical and deep learning inpainting methods for optimal MRI reconstruction.
Regularly assess the impact of image quality on classification accuracy in clinical settings.

Clinical Scorecard: A Smart Gradient-Directed Hybrid Inpainting Approach for Reconstructing Brain MRI and Classifying Alzheimer's Disease in Integrated Healthcare Networks